Method of Measuring Adaptive Immunity

ABSTRACT

A method of measuring immunocompetence is described. This method provides a means for assessing the effects of diseases or conditions that compromise the immune system and of therapies aimed to reconstitute it. This method is based on quantifying T-cell diversity by calculating the number of diverse T-cell receptor (TCR) beta chain variable regions from blood cells.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No. 13/960,761, filed on Aug. 6, 2013, which is a continuation of U.S. application Ser. No. 12/794,507, filed on Jun. 4, 2010, which claims the benefit of U.S. Provisional Application No. 61/220,344, filed on Jun. 25, 2009, which are all herein incorporated by reference in their entireties.

TECHNICAL FIELD

What is described is a method to measure the adaptive immunity of a patient by analyzing the diversity of T cell receptor genes or antibody genes using large scale sequencing of nucleic acid extracted from adaptive immune system cells.

BACKGROUND

Immunocompetence is the ability of the body to produce a normal immune response (i.e., antibody production and/or cell-mediated immunity) following exposure to a pathogen, which might be a live organism (such as a bacterium or fungus), a virus, or specific antigenic components isolated from a pathogen and introduced in a vaccine. Immunocompetence is the opposite of immunodeficiency or immuno-incompetent or immunocompromised. Several examples would be a newborn that does not yet have a fully functioning immune system but may have maternally transmitted antibody (immunodeficient); a late stage AIDS patient with a failed or failing immune system (immuno-incompetent); a transplant recipient taking medication so their body will not reject the donated organ (immunocompromised); age-related attenuation of T cell function in the elderly; or individuals exposed to radiation or chemotherapeutic drugs. There may be cases of overlap but these terms are all indicators of a dysfunctional immune system. In reference to lymphocytes, immunocompetence means that a B cell or T cell is mature and can recognize antigens and allow a person to mount an immune response.

Immunocompetence depends on the ability of the adaptive immune system to mount an immune response specific for any potential foreign antigens, using the highly polymorphic receptors encoded by B cells (immunoglobulins, Igs) and T cells (T cell receptors, TCRs).

Igs expressed by B cells are proteins consisting of four polypeptide chains, two heavy chains (H chains) and two light chains (L chains), forming an H₂L₂ structure. Each pair of H and L chains contains a hypervariable domain, consisting of a V_(L) and a V_(H) region, and a constant domain. The H chains of Igs are of several types, μ, δ, γ, α, and β. The diversity of Igs within an individual is mainly determined by the hypervariable domain. The V domain of H chains is created by the combinatorial joining of three types of germline gene segments, the V_(H), D_(H), and J_(H) segments. Hypervariable domain sequence diversity is further increased by independent addition and deletion of nucleotides at the V_(H)-D_(H), D_(H)-J_(H), and V_(H)-J_(H) junctions during the process of Ig gene rearrangement. In this respect, immunocompetence is reflected in the diversity of Igs.

TCRs expressed by αβ T cells are proteins consisting of two transmembrane polypeptide chains (α and β), expressed from the TCRA and TCRB genes, respectively. Similar TCR proteins are expressed in gamma-delta T cells, from the TCRD and TCRG loci. Each TCR peptide contains variable complementarity determining regions (CDRs), as well as framework regions (FRs) and a constant region. The sequence diversity of αβ T cells is largely determined by the amino acid sequence of the third complementarity-determining region (CDR3) loops of the α and β chain variable domains, which diversity is a result of recombination between variable (V_(β)), diversity (D_(β)), and joining (J_(β)) gene segments in the β chain locus, and between analogous V_(α) and J_(α) gene segments in the α chain locus, respectively. The existence of multiple such gene segments in the TCR α and β chain loci allows for a large number of distinct CDR3 sequences to be encoded. CDR3 sequence diversity is further increased by independent addition and deletion of nucleotides at the V_(β)-D_(β), D_(β)-J_(β), and V_(α)-J_(α) junctions during the process of TCR gene rearrangement. In this respect, immunocompetence is reflected in the diversity of TCRs.

There exists a long-felt need for methods of assessing or measuring the adaptive immune system of patients in a variety of settings, whether immunocompetence in the immunocompromised, or dysregulated adaptive immunity in autoimmune disease. A demand exists for methods of diagnosing a disease state or the effects of aging by assessing the immunocompetence of a patient. In the same way results of therapies that modify the immune system need to be monitored by assessing the immunocompetence of the patient while undergoing the treatment. Conversely, a demand exists for methods to monitor the adaptive immune system in the context of autoimmune disease flares and remissions, in order to monitor response to therapy, or the need to initiate prophylactic therapy pre-symptomatically.

SUMMARY

One aspect of the invention is composition comprising:

-   -   a multiplicity of V-segment primers, wherein each primer         comprises a sequence that is complementary to a single         functional V segment or a small family of V segments; and     -   a multiplicity of J-segment primers, wherein each primer         comprises a sequence that is complementary to a J segment;         wherein the V segment and J-segment primers permit amplification         of a TCR CDR3 region by a multiplex polymerase chain reaction         (PCR) to produce a multiplicity of amplified DNA molecules         sufficient to quantify the diversity of the TCR genes. One         embodiment of the invention is the composition, wherein each         V-segment primer comprises a sequence that is complementary to a         single Vβ segment, and each J segment primer comprises a         sequence that is complementary to a Jβ segment, and wherein V         segment and J-segment primers permit amplification of a TCRβ         CDR3 region. Another embodiment is the composition, wherein each         V-segment primer comprises a sequence that is complementary to a         single functional Vα segment, and each J segment primer         comprises a sequence that is complementary to a Jα segment, and         wherein V segment and J-segment primers permit amplification of         a TCRα CDR3 region.

Another embodiment of the invention is the composition, wherein the V segment primers hybridize with a conserved segment, and have similar annealing strength. Another embodiment is wherein the V segment primer is anchored at position −43 in the Vβ segment relative to the recombination signal sequence (RSS). Another embodiment is wherein the multiplicity of V segment primers consist of at least 45 primers specific to 45 different Vβ genes. Another embodiment is wherein the V segment primers have sequences that are selected from the group consisting of SEQ ID NOS:1-45. Another embodiment is wherein the V segment primers have sequences that are selected from the group consisting of SEQ ID NOS:58-102. Another embodiment is wherein there is a V segment primer for each Vβ segment.

Another embodiment of the invention is the composition, wherein the J segment primers hybridize with a conserved framework region element of the JP segment, and have similar annealing strength. The composition of claim 2, wherein the multiplicity of J segment primers consist of at least thirteen primers specific to thirteen different JP genes. Another embodiment is The composition of claim 2, wherein the J segment primers have sequences that are selected from the group consisting of SEQ ID NOS:46-57. Another embodiment is wherein the J segment primers have sequences that are selected from the group consisting of SEQ ID NOS:102-113. Another embodiment is wherein there is a J segment primer for each JP segment. Another embodiment is wherein all J segment primers anneal to the same conserved motif.

Another embodiment of the invention is the composition, wherein the amplified DNA molecule starts from said conserved motif and amplifies adequate sequence to diagnostically identify the J segment and includes the CDR3 junction and extends into the V segment. Another embodiment is wherein the amplified JP gene segments each have a unique four base tag at positions +11 through +14 downstream of the RSS site. Another aspect of the invention is the composition further comprising a set of sequencing oligonucleotides, wherein the sequencing oligonucleotides hybridize to a regions within the amplified DNA molecules. An embodiment is wherein the sequencing oligonucleotides hybridize adjacent to a four base tag within the amplified JP gene segments at positions +11 through +14 downstream of the RSS site. Another embodiment is wherein the sequencing oligonucleotides are selected from the group consisting of SEG ID NOS:58-70. Another embodiment is wherein the V-segment or J-segment are selected to contain a sequence error-correction by merger of closely related sequences. Another embodiment is the composition, further comprising a universal C segment primer for generating cDNA from mRNA.

Another aspect of the invention is a composition comprising:

-   -   a multiplicity of V segment primers, wherein each V segment         primer comprises a sequence that is complementary to a single         functional V segment or a small family of V segments; and     -   a multiplicity of J segment primers, wherein each J segment         primer comprises a sequence that is complementary to a J         segment;

wherein the V segment and J segment primers permit amplification of the TCRG CDR3 region by a multiplex polymerase chain reaction (PCR) to produce a multiplicity of amplified DNA molecules sufficient to quantify the diversity of antibody heavy chain genes.

Another aspect of the invention is a composition comprising:

-   -   a multiplicity of V segment primers, wherein each V segment         primer comprises a sequence that is complementary to a single         functional V segment or a small family of V segments; and     -   a multiplicity of J segment primers, wherein each J segment         primer comprises a sequence that is complementary to a J         segment;

wherein the V segment and J segment primers permit amplification of antibody heavy chain (IGH) CDR3 region by a multiplex polymerase chain reaction (PCR) to produce a multiplicity of amplified DNA molecules sufficient to quantify the diversity of antibody heavy chain genes.

Another aspect of the invention is a composition comprising:

-   -   a multiplicity of V segment primers, wherein each V segment         primer comprises a sequence that is complementary to a single         functional V segment or a small family of V segments; and     -   a multiplicity of J segment primers, wherein each J segment         primer comprises a sequence that is complementary to a J         segment;

wherein the V segment and J segment primers permit amplification of antibody light chain (IGL) V_(L) region by a multiplex polymerase chain reaction (PCR) to produce a multiplicity of amplified DNA molecules sufficient to quantify the diversity of antibody light chain genes.

Another aspect of the invention is a method comprising:

-   -   selecting a multiplicity of V segment primers, wherein each V         segment primer comprises a sequence that is complementary to a         single functional V segment or a small family of V segments; and     -   selecting a multiplicity of J segment primers, wherein each J         segment primer comprises a sequence that is complementary to a J         segment;     -   combining the V segment and J segment primers with a sample of         genomic DNA to permit amplification of a CDR3 region by a         multiplex polymerase chain reaction (PCR) to produce a         multiplicity of amplified DNA molecules sufficient to quantify         the diversity of the TCR genes.

One embodiment of the invention is the method wherein each V segment primer comprises a sequence that is complementary to a single functional Vβ segment, and each J segment primer comprises a sequence that is complementary to a Jβ segment; and wherein combining the V segment and J segment primers with a sample of genomic DNA permits amplification of a TCR CDR3 region by a multiplex polymerase chain reaction (PCR) and produces a multiplicity of amplified DNA molecules. Another embodiment is wherein each V segment primer comprises a sequence that is complementary to a single functional Vα segment, and each J segment primer comprises a sequence that is complementary to a Jα segment; and wherein combining the V segment and J segment primers with a sample of genomic DNA permits amplification of a TCR CDR3 region by a multiplex polymerase chain reaction (PCR) and produces a multiplicity of amplified DNA molecules.

Another embodiment of the invention is the method further comprising a step of sequencing the amplified DNA molecules. Another embodiment is wherein the sequencing step utilizes a set of sequencing oligonucleotides, that hybridize to regions within the amplified DNA molecules. Another embodiment is the method, further comprising a step of calculating the total diversity of TCRβ CDR3 sequences among the amplified DNA molecules. Another embodiment is wherein the method shows that the total diversity of a normal human subject is greater than 1*10⁶ sequences, greater than 2*10⁶ sequences, or greater than 3*10⁶ sequences.

Another aspect of the invention is a method of diagnosing immunodeficiency in a human patient, comprising measuring the diversity of TCR CDR3 sequences of the patient, and comparing the diversity of the subject to the diversity obtained from a normal subject. An embodiment of the invention is the method, wherein measuring the diversity of TCR sequences comprises the steps of:

-   -   selecting a multiplicity of V segment primers, wherein each V         segment primer comprises a sequence that is complementary to a         single functional V segment or a small family of V segments; and     -   selecting a multiplicity of J segment primers, wherein each J         segment primer comprises a sequence that is complementary to a J         segment;     -   combining the V segment and J segment primers with a sample of         genomic DNA to permit amplification of a TCR CDR3 region by a         multiplex polymerase chain reaction (PCR) to produce a         multiplicity of amplified DNA molecules;     -   sequencing the amplified DNA molecules;     -   calculating the total diversity of TCR CDR3 sequences among the         amplified DNA molecules.

An embodiment of the invention is the method, wherein comparing the diversity is determined by calculating using the following equation:

$\begin{matrix} {{\Delta \; (t)} = {{\sum\limits_{x}{E\left( n_{x} \right)}_{{{measurement}\mspace{14mu} 1} + 2}} - {\sum\limits_{x}{E\left( n_{x} \right)}_{{measurement}\mspace{14mu} 2}}}} \\ {= {S{\int_{0}^{\infty}{{^{- \lambda}\left( {1 - ^{{- \lambda}\; t}} \right)}{{G(\lambda)}}}}}} \end{matrix}$

wherein G(λ) is the empirical distribution function of the parameters λ₁, . . . , Z_(S), n_(x) is the number of clonotypes sequenced exactly x times, and

${E\left( n_{x} \right)} = {S{\int_{0}^{\infty}{\left( \frac{^{- \lambda}\lambda^{x}}{x!} \right){{{G(\lambda)}}.}}}}$

Another embodiment of the invention is the method, wherein the diversity of at least two samples of genomic DNA are compared. Another embodiment is wherein one sample of genomic DNA is from a patient and the other sample is from a normal subject. Another embodiment is wherein one sample of genomic DNA is from a patient before a therapeutic treatment and the other sample is from the patient after treatment. Another embodiment is wherein the two samples of genomic DNA are from the same patient at different times during treatment. Another embodiment is wherein a disease is diagnosed based on the comparison of diversity among the samples of genomic DNA. Another embodiment is wherein the immunocompetence of a human patient is assessed by the comparison.

DETAILED DESCRIPTION OF ILLUSTRATIVE EMBODIMENTS

The TCR and Ig genes can generate millions of distinct proteins via somatic mutation. Because of this diversity-generating mechanism, the hypervariable complementarity determining regions of these genes can encode sequences that can interact with millions of ligands, and these regions are linked to a constant region that can transmit a signal to the cell indicating binding of the protein's cognate ligand.

The adaptive immune system employs several strategies to generate a repertoire of T- and B-cell antigen receptors with sufficient diversity to recognize the universe of potential pathogens. In αβ and γδ T cells, which primarily recognize peptide antigens presented by MHC molecules, most of this receptor diversity is contained within the third complementarity-determining region (CDR3) of the T cell receptor (TCR) α and β chains (or γ and δ chains). Although it has been estimated that the adaptive immune system can generate up to 10¹⁸ distinct TCR αβ pairs, direct experimental assessment of TCR CDR3 diversity has not been possible.

What is described herein is a novel method of measuring TCR CDR3 diversity that is based on single molecule DNA sequencing, and use this approach to sequence the CDR3 regions in millions of rearranged TCRβ genes isolated from peripheral blood T cells of two healthy adults.

The ability of the adaptive immune system to mount an immune response specific for any of the vast number of potential foreign antigens to which an individual might be exposed relies on the highly polymorphic receptors encoded by B cells (immunoglobulins) and T cells (T cell receptors; TCRs). The TCRs expressed by αβ T cells, which primarily recognize peptide antigens presented by major histocompatibility complex (MHC) class I and II molecules, are heterodimeric proteins consisting of two transmembrane polypeptide chains (α and β), each containing one variable and one constant domain. The peptide specificity of αβ T cells is in large part determined by the amino acid sequence encoded in the third complementarity-determining region (CDR3) loops of the α and β chain variable domains. The CDR3 regions of the β and α chains are formed by recombination between noncontiguous variable (V_(β)), diversity (D_(β)), and joining (J_(β)) gene segments in the β chain locus, and between analogous V_(α) and J_(α) gene segments in the α chain locus, respectively. The existence of multiple such gene segments in the TCR α and β chain loci allows for a large number of distinct CDR3 sequences to be encoded. CDR3 sequence diversity is further increased by template-independent addition and deletion of nucleotides at the V_(β)-D_(β), D_(β)-J_(β), and V_(α)-J_(α) junctions during the process of TCR gene rearrangement.

Previous attempts to assess the diversity of receptors in the adult human αβ T cell repertoire relied on examining rearranged TCR α and β chain genes expressed in small, well-defined subsets of the repertoire, followed by extrapolation of the diversity present in these subsets to the entire repertoire, to estimate approximately 10⁶ unique TCRβ chain CDR3 sequences per individual, with 10-20% of these unique TCRβ CDR3 sequences expressed by cells in the antigen-experienced CD45RO⁺ compartment. The accuracy and precision of this estimate is severely limited by the need to extrapolate the diversity observed in hundreds of sequences to the entire repertoire, and it is possible that the actual number of unique TCRβ chain CDR3 sequences in the αβ T cell repertoire is significantly larger than 1×10⁶.

Recent advances in high-throughput DNA sequencing technology have made possible significantly deeper sequencing than capillary-based technologies. A complex library of template molecules carrying universal PCR adapter sequences at each end is hybridized to a lawn of complementary oligonucleotides immobilized on a solid surface. Solid phase PCR is utilized to amplify the hybridized library, resulting in millions of template clusters on the surface, each comprising multiple (1,000) identical copies of a single DNA molecule from the original library. A 30-54 bp interval in the molecules in each cluster is sequenced using reversible dye-termination chemistry, to permit simultaneous sequencing from genomic DNA of the rearranged TCRβ chain CDR3 regions carried in millions of T cells. This approach enables direct sequencing of a significant fraction of the uniquely rearranged TCRβ CDR3 regions in populations of αβ T cells, which thereby permits estimation of the relative frequency of each CDR3 sequence in the population.

Accurate estimation of the diversity of TCRβ CDR3 sequences in the entire αβ T cell repertoire from the diversity measured in a finite sample of T cells requires an estimate of the number of CDR3 sequences present in the repertoire that were not observed in the sample. TCRβ chain CDR3 diversity in the entire αβ T cell repertoire were estimated using direct measurements of the number of unique TCRβ CDR3 sequences observed in blood samples containing millions of αβ T cells. The results herein identify a lower bound for TCRβ CDR3 diversity in the CD4⁺ and CD8⁺ T cell compartments that is several fold higher than previous estimates. In addition, the results herein demonstrat that there are at least 1.5×10⁶ unique TCRβ CDR3 sequences in the CD45RO⁺ compartment of antigen-experienced T-cells, a large proportion of which are present at low relative frequency. The existence of such a diverse population of TCRβ CDR3 sequences in antigen-experienced cells has not been previously demonstrated.

The diverse pool of TCRβ chains in each healthy individual is a sample from an estimated theoretical space of greater than 10¹¹ possible sequences. However, the realized set of rearranged of TCRs is not evenly sampled from this theoretical space. Different Vβ's and Jβ's are found with over a thousand-fold frequency difference. Additionally, the insertion rates of nucleotides are strongly biased. This reduced space of realized TCRβ sequences leads to the possibility of shared β chains between people. With the sequence data generated by the methods described herein, the in vivo J usage, V usage, mono- and di-nucleotide biases, and position dependent amino acid usage can be computed. These biases significantly narrow the size of the sequence space from which TCRβ are selected, suggesting that different individuals share TCRβ chains with identical amino acid sequences. Results herein show that many thousands of such identical sequences are shared pairwise between individual human genomes.

The assay technology uses two pools of primers to provide for a highly multiplexed PCR reaction. The “forward” pool has a primer specific to each V segment in the gene (several primers targeting a highly conserved region are used, to simultaneously capture many V segments). The “reverse” pool primers anneal to a conserved sequence in the joining (“J”) segment. The amplified segment pool includes adequate sequence to identify each J segment and also to allow for a J-segment-specific primer to anneal for resequencing. This enables direct observation of a large fraction of the somatic rearrangements present in an individual. This in turn enables rapid comparison of the TCR repertoire in individuals with an autoimmune disorder (or other target disease indication) against the TCR repertoire of controls.

The adaptive immune system can in theory generate an enormous diversity of T cell receptor CDR3 sequences—far more than are likely to be expressed in any one individual at any one time. Previous attempts to measure what fraction of this theoretical diversity is actually utilized in the adult αβ T cell repertoire, however, have not permitted accurate assessment of the diversity. What is described herein is the development of a novel approach to this question that is based on single molecule DNA sequencing and an analytic computational approach to estimation of repertoire diversity using diversity measurements in finite samples. The analysis demonstrated that the number of unique TCRβ CDR3 sequences in the adult repertoire significantly exceeds previous estimates based on exhaustive capillary sequencing of small segments of the repertoire. The TCRβ chain diversity in the CD45RO⁻ population (enriched for naïve T cells) observed using the methods described herein is five-fold larger than previously reported. A major discovery is the number of unique TCRβ CDR3 sequences expressed in antigen-experienced CD45RO⁺ T cells—the results herein show that this number is between 10 and 20 times larger than expected based on previous results of others. The frequency distribution of CDR3 sequences in CD45RO⁺ cells suggests that the T cell repertoire contains a large number of clones with a small clone size.

The results herein show that the realized set of TCRβ chains are sampled non-uniformly from the huge potential space of sequences. In particular, the β chains sequences closer to germ line (few insertions and deletions at the V-D and D-J boundaries) appear to be created at a relatively high frequency. TCR sequences close to germ line are shared between different people because the germ line sequence for the V's, D's, and J's are shared, modulo a small number of polymorphisms, among the human population.

The T cell receptors expressed by mature αβ T cells are heterodimers whose two constituent chains are generated by independent rearrangement events of the TCR α and β chain variable loci. The α chain has less diversity than the β chain, so a higher fraction of α's are shared between individuals, and hundreds of exact TCR αβ receptors are shared between any pair of individuals.

Cells

B cells and T cells can be obtained from a variety of tissue samples including marrow, thymus, lymph glands, peripheral tissues and blood, but peripheral blood is most easily accessed. Peripheral blood samples are obtained by phlebotomy from subjects. Peripheral blood mononuclear cells (PBMC) are isolated by techniques known to those of skill in the art, e.g., by Ficoll-Hypaque® density gradient separation. Preferably, whole PBMCs are used for analysis. The B and/or T lymphocytes, instead, may be flow sorted into multiple compartments for each subject: e.g. CD8⁺CD45RO^(+/−) and CD4⁺CD45RO^(+/−) using fluorescently labeled anti-human antibodies, e.g, CD4 FITC (clone M-T466, Miltenyi Biotec), CD8 PE (clone RPA-T8, BD Biosciences), CD45RO ECD (clone UCHL-1, Beckman Coulter), and CD45RO APC (clone UCHL-1, BD Biosciences). Staining of total PBMCs may be done with the appropriate combination of antibodies, followed by washing cells before analysis. Lymphocyte subsets can be isolated by FACS sorting, e.g., by a BD FACSAria™ cell-sorting system (BD Biosciences) and by analyzing results with FlowJo software (Treestar Inc.), and also by conceptually similar methods involving specific antibodies immobilized to surfaces or beads.

Nucleic Acid Extraction

Total genomic DNA is extracted from cells, e.g., by using the QIAamp® DNA blood Mini Kit (QIAGEN®). The approximate mass of a single haploid genome is 3 μg. Preferably, at least 100,000 to 200,000 cells are used for analysis of diversity, i.e., about 0.6 to 1.2 μg DNA from diploid T cells. Using PBMCs as a source, the number of T cells can be estimated to be about 30% of total cells.

Alternatively, total nucleic acid can be isolated from cells, including both genomic DNA and mRNA. If diversity is to be measured from mRNA in the nucleic acid extract, the mRNA must be converted to cDNA prior to measurement. This can readily be done by methods of one of ordinary skill.

DNA Amplification

A multiplex PCR system is used to amplify rearranged TCR loci from genomic DNA, preferably from a CDR3 region, more preferably from a TCRα, TCRγ or TCRδ CDR3 region, most preferably from a TCRβ CDR3 region.

In general, a multiplex PCR system may use at least 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, or 25, preferably 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, or 39, most preferably 40, 41, 42, 43, 44, or 45 forward primers, in which each forward primer is specific to a sequence corresponding to one or more TRB V region segments shown in SEQ ID NOS:114-248; and at least 3, 4, 5, 6, or 7, preferably 8, 9, 10, 11, 12 or 13 reverse primers, in which each reverse primer is specific to a sequence corresponding to one or more TRB J region segments shown in SEQ ID NOS:249-261. Most preferably, there is a J segment primer for every J segment.

Preferably, the primers are designed not to cross an intron/exon boundary. The forward primers must preferably anneal to the V segments in a region of relatively strong sequence conservation between V segments so as to maximize the conservation of sequence among these primers. Accordingly, this minimizes the potential for differential annealing properties of each primer, and so that the amplified region between V and J primers contains sufficient TCR V sequence information to identify the specific V gene segment used.

Preferably, the J segment primers hybridize with a conserved element of the J segment, and have similar annealing strength. Most preferably, all J segment primers anneal to the same conserved framework region motif. The forward and reverse primers are both preferably modified at the 5′ end with the universal forward primer sequence compatible with a DNA sequencer.

For example, a multiplex PCR system may use 45 forward primers (Table 1), each specific to a functional TCR Vβ segment, and thirteen reverse primers (Table 2), each specific to a TCR Jβ segment. Xn and Yn correspond to polynucleotides of lengths n and m, respectively, which would be specific to the single molecule sequencing technology being used to read out the assay.

TABLE 1 TCR-Vβ Forward primer sequences SEQ TRBV gene ID segment(s) NO: Primer sequence* TRBV2 1 XnTCAAATTTCACTCTGAAGATCCGGTC CACAA TRBV3-1 2 XnGCTCACTTAAATCTTCACATCAATTC CCTGG TRBV4-1 3 XnCTTAAACCTTCACCTACACGCCCTGC TRBV(4-2, 4-3) 4 XnCTTATTCCTTCACCTACACACCCTGC TRBV5-1 5 XnGCTCTGAGATGAATGTGAGCACCTTG TRBV5-3 6 XnGCTCTGAGATGAATGTGAGTGCCTTG TRBV(5-4, 5-5,  7 XnGCTCTGAGCTGAATGTGAACGCCTTG 5-6, 5-7, 5-8) TRBV6-1 8 XnTCGCTCAGGCTGGAGTCGGCTG TRBV(6-2, 6-3) 9 XnGCTGGGGTTGGAGTCGGCTG TRBV6-4 10 XnCCCTCACGTTGGCGTCTGCTG TRBV6-5 11 XnGCTCAGGCTGCTGTCGGCTG TRBV6-6 12 XnCGCTCAGGCTGGAGTTGGCTG TRBV6-7 13 XnCCCCTCAAGCTGGAGTCAGCTG TRBV6-8 14 XnCACTCAGGCTGGTGTCGGCTG TRBV6-9 15 XnCGCTCAGGCTGGAGTCAGCTG TRBV7-1 16 XnCCACTCTGAAGTTCCAGCGCACAC TRBV7-2 17 XnCACTCTGACGATCCAGCGCACAC TRBV7-3 18 XnCTCTACTCTGAAGATCCAGCGCACAG TRBV7-4 19 XnCCACTCTGAAGATCCAGCGCACAG TRBV7-6 20 XnCACTCTGACGATCCAGCGCACAG TRBV7-7 21 XnCCACTCTGACGATTCAGCGCACAG TRBV7-8 22 XnCCACTCTGAAGATCCAGCGCACAC TRBV7-9 23 XnCACCTTGGAGATCCAGCGCACAG TRBV9 24 XnGCACTCTGAACTAAACCTGAGCTCTC TG TRBV10-1 25 XnCCCCTCACTCTGGAGTCTGCTG TRBV10-2 26 XnCCCCCTCACTCTGGAGTCAGCTA TRBV10-3 27 XnCCTCCTCACTCTGGAGTCCGCTA TRBV(11-1, 11-3) 28 XnCCACTCTCAAGATCCAGCCTGCAG TRBV11-2 29 XnCTCCACTCTCAAGATCCAGCCTGCAA TRBV(12-3, 12-4, 30 XnCCACTCTGAAGATCCAGCCCTCAG 12-5) TRBV13 31 XnCATTCTGAACTGAACATGAGCTCCTT GG TRBV14 32 XnCTACTCTGAAGGTGCAGCCTGCAG TRBV15 33 XnGATAACTTCCAATCCAGGAGGCCGAA CA TRBV16 34 XnCTGTAGCCTTGAGATCCAGGCTACGA TRBV17 35 XnCTTCCACGCTGAAGATCCATCCCG TRBV18 36 XnGCATCCTGAGGATCCAGCAGGTAG TRBV19 37 XnCCTCTCACTGTGACATCGGCCC TRBV20-1 38 XnCTTGTCCACTCTGACAGTGACCAGTG TRBV23-1 39 XnCAGCCTGGCAATCCTGTCCTCAG TRBV24-1 40 XnCTCCCTGTCCCTAGAGTCTGCCAT TRBV25-1 41 XnCCCTGACCCTGGAGTCTGCCA TRBV27 42 XnCCCTGATCCTGGAGTCGCCCA TRBV28 43 XnCTCCCTGATTCTGGAGTCCGCCA TRBV29-1 44 XnCTAACATTCTCAACTCTGACTGTGAG CAACA TRBV30 45 XnCGGCAGTTCATCCTGAGTTCTAAGAA GC

TABLE 2 TCR-Jβ Reverse Primer Sequences TRBJ gene SEQ ID segment NO: Primer sequence* TRBJ1-1 46 YmTTACCTACAACTGTGAGTCTGGTGCCTTGTC CAAA TRBJ1-2 47 YmACCTACAACGGTTAACCTGGTCCCCGAACCG AA TRBJ1-3 48 YmACCTACAACAGTGAGCCAACTTCCCTCTCCA AA TRBJ1-4 49 YmCCAAGACAGAGAGCTGGGTTCCACTGCCAAA TRBJ1-5 483 YmACCTAGGATGGAGAGTCGAGTCCCATCACCA AA TRBJ1-6 50 YmCTGTCACAGTGAGCCTGGTCCCGTTCCCAAA TRBJ2-1 51 YmCGGTGAGCCGTGTCCCTGGCCCGAA TRBJ2-2 52 YmCCAGTACGGTCAGCCTAGAGCCTTCTCCAAA TRBJ2-3 53 YmACTGTCAGCCGGGTGCCTGGGCCAAA TRBJ2-4 54 YmAGAGCCGGGTCCCGGCGCCGAA TRBJ2-5 55 YmGGAGCCGCGTGCCTGGCCCGAA TRBJ2-6 56 YmGTCAGCCTGCTGCCGGCCCCGAA TRBJ2-7 57 YmGTGAGCCTGGTGCCCGGCCCGAA

The 45 forward PCR primers of Table 1 are complementary to each of the 48 functional Variable segments, and the thirteen reverse PCR primers of Table 2 are complementary to each of the functional joining (J) gene segments from the TRB locus (TRBJ). The TRB V region segments are identified in the Sequence Listing at SEQ ID NOS:114-248 and the TRB J region segments are at SEQ ID NOS:249-261. The primers have been designed such that adequate information is present within the amplified sequence to identify both the V and J genes uniquely (>40 base pairs of sequence upstream of the V gene recombination signal sequence (RSS), and >30 base pairs downstream of the J gene RSS). Alternative primers may be selected by one of ordinary skill from the V and J regions of the genes of each TCR subunit.

The forward primers are modified at the 5′ end with the universal forward primer sequence compatible with the DNA sequencer (Xn of Table 1). Similarly, all of the reverse primers are modified with a universal reverse primer sequence (Ym of Table 2). One example of such universal primers is shown in Tables 3 and 4, for the Illumina GAII single-end read sequencing system. The 45 TCR Vβ forward primers anneal to the Vβ segments in a region of relatively strong sequence conservation between Vβ segments so as to maximize the conservation of sequence among these primers.

TABLE 3 TCR-Vβ Forward primer sequences SEQ TRBV gene ID segment(s) NO: Primer sequence* TRBV2 58 CAAGCAGAAGACGGCATACGAGCTCTTCCGATCTTCAAATTTCACTCTGAAGATCCGGTCCACAA TRBV3-1 59 CAAGCAGAAGACGGCATACGAGCTCTTCCGATCTGCTCACTTAAATCTTCACATCAATTCCCTGG TRBV4-1 60 CAAGCAGAAGACGGCATACGAGCTCTTCCGATCTCTTAAACCTTCACCTACACGCCCTGC TRBV(4-2, 4-3) 61 CAAGCAGAAGACGGCATACGAGCTCTTCCGATCTCTTATTCCTTCACCTACACACCCTGC TRBV5-1 62 CAAGCAGAAGACGGCATACGAGCTCTTCCGATCTGCTCTGAGATGAATGTGAGCACCTTG TRBV5-3 63 CAAGCAGAAGACGGCATACGAGCTCTTCCGATCTGCTCTGAGATGAATGTGAGTGCCTTG TRBV(5-4, 5-5, 64 CAAGCAGAAGACGGCATACGAGCTCTTCCGATCTGCTCTGAGCTGAATGTGAACGCCTTG 5-6,5-7,5-8) TRBV6-1 65 CAAGCAGAAGACGGCATACGAGCTCTTCCGATCTTCGCTCAGGCTGGAGTCGGCTG TRBV(6-2, 6-3) 66 CAAGCAGAAGACGGCATACGAGCTCTTCCGATCTGCTGGGGTTGGAGTCGGCTG TRBV6-4 67 CAAGCAGAAGACGGCATACGAGCTCTTCCGATCTCCCTCACGTTGGCGTCTGCTG TRBV6-5 68 CAAGCAGAAGACGGCATACGAGCTCTTCCGATCTGCTCAGGCTGCTGTCGGCTG TRBV6-6 69 CAAGCAGAAGACGGCATACGAGCTCTTCCGATCTCGCTCAGGCTGGAGTTGGCTG TRBV6-7 70 CAAGCAGAAGACGGCATACGAGCTCTTCCGATCTCCCCTCAAGCTGGAGTCAGCTG TRBV6-8 71 CAAGCAGAAGACGGCATACGAGCTCTTCCGATCTCACTCAGGCTGGTGTCGGCTG TRBV6-9 72 CAAGCAGAAGACGGCATACGAGCTCTTCCGATCTCGCTCAGGCTGGAGTCAGCTG TRBV7-1 73 CAAGCAGAAGACGGCATACGAGCTCTTCCGATCTCCACTCTGAAGTTCCAGCGCACAC TRBV7-2 74 CAAGCAGAAGACGGCATACGAGCTCTTCCGATCTCACTCTGACGATCCAGCGCACAC TRBV7-3 75 CAAGCAGAAGACGGCATACGAGCTCTTCCGATCTCTCTACTCTGAAGATCCAGCGCACAG TRBV7-4 76 CAAGCAGAAGACGGCATACGAGCTCTTCCGATCTCCACTCTGAAGATCCAGCGCACAG TRBV7-6 77 CAAGCAGAAGACGGCATACGAGCTCTTCCGATCTCACTCTGACGATCCAGCGCACAG TRBV7-7 78 CAAGCAGAAGACGGCATACGAGCTCTTCCGATCTCCACTCTGACGATTCAGCGCACAG TRBV7-8 79 CAAGCAGAAGACGGCATACGAGCTCTTCCGATCTCCACTCTGAAGATCCAGCGCACAC TRBV7-9 80 CAAGCAGAAGACGGCATACGAGCTCTTCCGATCTCACCTTGGAGATCCAGCGCACAG TRBV9 81 CAAGCAGAAGACGGCATACGAGCTCTTCCGATCTGCACTCTGAACTAAACCTGAGCTCTCTG TRBV10-1 82 CAAGCAGAAGACGGCATACGAGCTCTTCCGATCTCCCCTCACTCTGGAGTCTGCTG TRBV10-2 83 CAAGCAGAAGACGGCATACGAGCTCTTCCGATCTCCCCCTCACTCTGGAGTCAGCTA TRBV10-3 84 CAAGCAGAAGACGGCATACGAGCTCTTCCGATCTCCTCCTCACTCTGGAGTCCGCTA TRBV(11-1,11-3) 85 CAAGCAGAAGACGGCATACGAGCTCTTCCGATCTCCACTCTCAAGATCCAGCCTGCAG TRBV11-2 86 CAAGCAGAAGACGGCATACGAGCTCTTCCGATCTCTCCACTCTCAAGATCCAGCCTGCAA TRBV(12-3,  87 CAAGCAGAAGACGGCATACGAGCTCTTCCGATCTCCACTCTGAAGATCCAGCCCTCAG 12-4, 12-5) TRBV13 88 CAAGCAGAAGACGGCATACGAGCTCTTCCGATCTCATTCTGAACTGAACATGAGCTCCTTGG TRBV14 89 CAAGCAGAAGACGGCATACGAGCTCTTCCGATCTCTACTCTGAAGGTGCAGCCTGCAG TRBV15 90 CAAGCAGAAGACGGCATACGAGCTCTTCCGATCTGATAACTTCCAATCCAGGAGGCCGAACA TRBV16 91 CAAGCAGAAGACGGCATACGAGCTCTTCCGATCTCTGTAGCCTTGAGATCCAGGCTACGA TRBV17 92 CAAGCAGAAGACGGCATACGAGCTCTTCCGATCTCTTCCACGCTGAAGATCCATCCCG TRBV18 93 CAAGCAGAAGACGGCATACGAGCTCTTCCGATCTGCATCCTGAGGATCCAGCAGGTAG TRBV19 94 CAAGCAGAAGACGGCATACGAGCTCTTCCGATCTCCTCTCACTGTGACATCGGCCC TRBV20-1 95 CAAGCAGAAGACGGCATACGAGCTCTTCCGATCTCTTGTCCACTCTGACAGTGACCAGTG TRBV23-1 96 CAAGCAGAAGACGGCATACGAGCTCTTCCGATCTCAGCCTGGCAATCCTGTCCTCAG TRBV24-1 97 CAAGCAGAAGACGGCATACGAGCTCTTCCGATCTCTCCCTGTCCCTAGAGTCTGCCAT TRBV25-1 98 CAAGCAGAAGACGGCATACGAGCTCTTCCGATCTCCCTGACCCTGGAGTCTGCCA TRBV27 99 CAAGCAGAAGACGGCATACGAGCTCTTCCGATCTCCCTGATCCTGGAGTCGCCCA TRBV28 100 CAAGCAGAAGACGGCATACGAGCTCTTCCGATCTCTCCCTGATTCTGGAGTCCGCCA TRBV29-1 101 CAAGCAGAAGACGGCATACGAGCTCTTCCGATCTCTAACATTCTCAACTCTGACTGTGAGCAACA TRBV30 102 CAAGCAGAAGACGGCATACGAGCTCTTCCGATCTCGGCAGTTCATCCTGAGTTCTAAGAAGC

TABLE 4 TCR-Jβ Reverse Primer Sequences TRBJ gene SEQ ID segment NO: Primer sequence* TRBJ1-1 103 AATGATACGGCGACCACCGAGATCTTTACCTACAACTGTGAGTCTGGTGCCTTGTCCAAA TRBJ1-2 468 AATGATACGGCGACCACCGAGATCTACCTACAACGGTTAACCTGGTCCCCGAACCGAA TRBJ1-3 104 AATGATACGGCGACCACCGAGATCTACCTACAACAGTGAGCCAACTTCCCTCTCCAAA TRBJ1-4 105 AATGATACGGCGACCACCGAGATCTCCAAGACAGAGAGCTGGGTTCCACTGCCAAA TRBJ1-5 484 AATGATACGGCGACCACCGAGATCTACCTAGGATGGAGAGTCGAGTCCCATCACCAAA TRBJ1-6 106 AATGATACGGCGACCACCGAGATCTCTGTCACAGTGAGCCTGGTCCCGTTCCCAAA TRBJ2-1 107 AATGATACGGCGACCACCGAGATCTCGGTGAGCCGTGTCCCTGGCCCGAA TRBJ2-2 108 AATGATACGGCGACCACCGAGATCTCCAGTACGGTCAGCCTAGAGCCTTCTCCAAA TRBJ2-3 109 AATGATACGGCGACCACCGAGATCTACTGTCAGCCGGGTGCCTGGGCCAAA TRBJ2-4 110 AATGATACGGCGACCACCGAGATCTAGAGCCGGGTCCCGGCGCCGAA TRBJ2-5 111 AATGATACGGCGACCACCGAGATCTGGAGCCGCGTGCCTGGCCCGAA TRBJ2-6 112 AATGATACGGCGACCACCGAGATCTGTCAGCCTGCTGCCGGCCCCGAA TRBJ2-7 113 AATGATACGGCGACCACCGAGATCTGTGAGCCTGGTGCCCGGCCCGAA *bold sequence indicates universal R oligonucleotide for the sequence analysis

The total PCR product for a rearranged TCRβ CDR3 region using this system is expected to be approximately 200 bp long. Genomic templates are PCR amplified using a pool of the 45 TCR Vβ F primers (the “VF pool”) and a pool of the twelve TCR Jβ R primers (the “JR pool”). For example, 50 μl PCR reactions may be used with 1.0 μM VF pool (22 nM for each unique TCR Vβ F primer), 1.0 μM R pool (77 nM for each unique TCRBJR primer), 1×QIAGEN Multiple PCR master mix (QIAGEN part number 206145), 10% Q-solution (QIAGEN), and 16 ng/ul gDNA.

The IGH primer set was designed to try to accommodate the potential for somatic hypermutation within the rearranged IGH genes, as is observed after initial stimulation of naïve B cells. Consequently all primers were designed to be slightly longer than normal, and to anchor the 3′ ends of each primer into highly conserved sequences of three or more nucleotides that should be resistant to both functional and non-functional somatic mutations.

The IGHJ reverse primers were designed to anchor the 3′ end of each PCR primer on a highly conserved GGGG sequence motif within the IGHJ segments. These sequences are shown in Table 5. Underlined sequence are ten base pairs in from RSS that may be deleted. These were excluded from barcode design. Bold sequence is the reverse complement of the IGH J reverse PCR primers. Italicized sequence is the barcode for J identity (eight barcodes reveal six genes, and two alleles within genes). Further sequence within underlined segment may reveal additional allelic identities.

TABLE 5 SEQ ID IgH J segment NO: Sequence >IGHJ4*01/1-48 452                ACTACTTTGA CTACTGGGGCCAAGGAACCCTGGTCACCGTCTCCTCAG >IGHJ4*03/1-48 453                GCTACTTTGA CTACTGGGGCCAAGGGACCCTGGTCACCGTCTCCTCAG >IGHJ4*02/1-48 454                ACTACTTTGA CTACTGGGGCCAGGGAACCCTGGTCACCGTCTCCTCAG >IGHJ3*01/1-50 455              TGATGCTTTTGATGTCTGGGGCCAAGGGACAATGGTCACCGTCTCTTCAG >IGHJ3*02/1-50 456              TGATGCTTTTGATATCTGGGGCCAAGGGACAATGGTCACCGTCTCTTCAG >IGHJ6*01/1-63 457 ATTACTACTACTACTACGGTATGGACGTCTGGGGGCAAGGGACCACGGTCACCGTCTCCTCAG >IGHJ6*02/1-62 458 ATTACTACTACTACTACGGTATGGACGTCTGGGGCCAAGGGACCACGGTCACCGTCTCCTCAG >IGHJ6*04/1-63 459 ATTACTACTACTACTACGGTATGGACGTCTGGGGCAAAGGGACCACGGTCACCGTCTCCTCAG >IGHJ6*03/1-62 460 ATTACTACTACTACTACTACATGGACGTCTGGGGCAAAGGGACCACGGTCACCGTCTCCTCAG >IGHJ2*01/1-53 461           CTACTGGTACTTCGATCTCTGGGGCCGTGGCACCCTGGTCACTGTCTCCTCAG >IGHJ5*01/1-51 462             ACAACTGGTTCGACTCCTGGGGCCAAGGAACCCTGGTCACCGTCTCCTCAG >IGHJ5*02/1-51 463             ACAACTGGTTCGACCCCTGGGGCCAGGGAACCCTGGTCACCGTCTCCTCAG >IGHJ1*01/1-52 464            GCTGAATACTTCCAGCACTGGGGCCAGGGCACCCTGGTCACCGTCTCCTCAG >IGHJ2P*01/1-61 465   CTACAAGTGCTTGGAGCACTGGGGCAGGGCAGCCCGGACACCGTCTCCCTGGGAACGTCAG >IGHJ1P*01/1-54 466          AAAGGTGCTGGGGGTCCCCTGAACCCGACCCGCCCTGAGACCGCAGCCACATCA >IGHJ3P*01/1-52 467            CTTGCGGTTGGACTTCCCAGCCGACAGTGGTGGTCTGGCTTCTGAGGGGTCA

Sequences of the IGHJ reverse PCR primers are shown in Table 6.

TABLE 6 IgH J SEQ ID segment NO: sequence >IGHJ4_1 421 TGAGGAGACGGTGACCAGGGTTCCTTGGCCC >IGHJ4_3 422 TGAGGAGACGGTGACCAGGGTCCCTTGGCCC >IGHJ4_2 423 TGAGGAGACGGTGACCAGGGTTCCCTGGCCC >IGHJ3_12 424 CTGAAGAGACGGTGACCATTGTCCCTTGGCCC >IGHJ6_1 425 CTGAGGAGACGGTGACCGTGGTCCCTTGCCCC >IGHJ6_2 426 TGAGGAGACGGTGACCGTGGTCCCTTGGCCC >IGHJ6_34 427 CTGAGGAGACGGTGACCGTGGTCCCTTTGCCC >IGHJ2_1 428 CTGAGGAGACAGTGACCAGGGTGCCACGGCCC >IGHJ5_1 429 CTGAGGAGACGGTGACCAGGGTTCCTTGGCCC >IGHJ5_2 430 CTGAGGAGACGGTGACCAGGGTTCCCTGGCCC >IGHJ1_1 431 CTGAGGAGACGGTGACCAGGGTGCCCTGGCCC

V primers were designed in a conserved in region of FR2 between the two conserved tryptophan (W) codons.

The primer sequences are anchored at the 3′ end on a tryptophan codon for all IGHV families that conserve this codon. his allows for the last three nucleotides (tryptophan's TGG) to anchor on sequence that is expected to be resistant to somatic hypermutation, providing a 3′ anchor of five out of six nucleotides for each primer. The upstream sequence is extended further than normal, and includes degenerate nucleotides to allow for mismatches induced by hypermutation (or between closely relate IGH V families) without dramatically changing the annealing characteristics of the primer, as shown in Table 7. The sequences of the V gene segments are SEQ ID NOS:262-420.

TABLE 7 IgH V SEQ ID segment NO: sequence >IGHV1 443 TGGGTGCACCAGGTCCANGNACAAGGGCTT GAGTGG >IGHV2 444 TGGGTGCGACAGGCTCGNGNACAACGCCTT GAGTGG >IGHV3 445 TGGGTGCGCCAGATGCCNGNGAAAGGCCTG GAGTGG >IGHV4 446 TGGGTCCGCCAGSCYCCNGNGAAGGGGCTG GAGTGG >IGHV5 447 TGGGTCCGCCAGGCTCCNGNAAAGGGGCTG GAGTGG >IGHV6 448 TGGGTCTGCCAGGCTCCNGNGAAGGGGCAG GAGTGG >IGH7_3.25p 449 TGTGTCCGCCAGGCTCCAGGGAATGGGCTG GAGTTGG >IGH8_3.54p 450 TCAGATTCCCAAGCTCCAGGGAAGGGGCTG GAGTGAG >IGH9_3.63p 451 TGGGTCAATGAGACTCTAGGGAAGGGGCTG GAGGGAG

Thermal cycling conditions may follow methods of those skilled in the art. For example, using a PCR Express thermal cycler (Hybaid, Ashford, UK), the following cycling conditions may be used: 1 cycle at 95° C. for 15 minutes, 25 to 40 cycles at 94° C. for 30 seconds, 59° C. for 30 seconds and 72° C. for 1 minute, followed by one cycle at 72° C. for 10 minutes.

Sequencing

Sequencing is achieved using a set of sequencing oligonucleotides that hybridize to a defined region within the amplified DNA molecules.

Preferably, the amplified J gene segments each have a unique four base tag at positions +11 through +14 downstream from the RSS site. Accordingly, the sequencing oligonucleotides hybridize adjacent to a four base tag within the amplified Jβ gene segments at positions +11 through +14 downstream of the RSS site.

For example, sequencing oligonucleotides for TCRB may be designed to anneal to a consensus nucleotide motif observed just downstream of this “tag”, so that the first four bases of a sequence read will uniquely identify the J segment (Table 8).

TABLE 8 Sequencing oligonucleotides Sequencing SEQ ID oligonucleotide NO: Oligonucleotide sequence Jseq 1-1 470 ACAACTGTGAGTCTGGTGCCTTGT CCAAAGAAA Jseq 1-2 471 ACAACGGTTAACCTGGTCCCCGAA CCGAAGGTG Jseq 1-3 472 ACAACAGTGAGCCAACTTCCCTCT CCAAAATAT Jseq 1-4 473 AAGACAGAGAGCTGGGTTCCACTG CCAAAAAAC Jseq 1-5 474 AGGATGGAGAGTCGAGTCCCATCA CCAAAATGC Jseq 1-6 475 GTCACAGTGAGCCTGGTCCCGTTC CCAAAGTGG Jseq 2-1 476 AGCACGGTGAGCCGTGTCCCTGGC CCGAAGAAC Jseq 2-2 477 AGTACGGTCAGCCTAGAGCCTTCT CCAAAAAAC Jseq 2-3 478 AGCACTGTCAGCCGGGTGCCTGGG CCAAAATAC Jseq 2-4 479 AGCACTGAGAGCCGGGTCCCGGCG CCGAAGTAC Jseq 2-5 480 AGCACCAGGAGCCGCGTGCCTGGC CCGAAGTAC Jseq 2-6 481 AGCACGGTCAGCCTGCTGCCGGCC CCGAAAGTC Jseq 2-7 482 GTGACCGTGAGCCTGGTGCCCGGC CCGAAGTAC

The information used to assign the J and V segment of a sequence read is entirely contained within the amplified sequence, and does not rely upon the identity of the PCR primers. These sequencing oligonucleotides were selected such that promiscuous priming of a sequencing reaction for one J segment by an oligonucleotide specific to another J segment would generate sequence data starting at exactly the same nucleotide as sequence data from the correct sequencing oligonucleotide. In this way, promiscuous annealing of the sequencing oligonucleotides did not impact the quality of the sequence data generated.

The average length of the CDR3 region, defined as the nucleotides between the second conserved cysteine of the V segment and the conserved phenylalanine of the J segment, is 35+/−3, so sequences starting from the Jβ segment tag will nearly always capture the complete V-D-J junction in a 50 base pair read.

TCR βJ gene segments are roughly 50 base pair in length. PCR primers that anneal and extend to mismatched sequences are referred to as promiscuous primers. The TCR Jβ Reverse PCR primers were designed to minimize overlap with the sequencing oligonucleotides to minimize promiscuous priming in the context of multiplex PCR. The 13 TCR Jβ reverse primers are anchored at the 3′ end on the consensus splice site motif, with minimal overlap of the sequencing primers. The TCR Jβ primers provide consistent annealing temperature using the sequencer program under default parameters.

For the sequencing reaction, the IGHJ sequencing primers extend three nucleotides across the conserved CAG sequences as shown in Table 9.

TABLE 9 IgH J SEQ ID segment NO: sequence >IGHJSEQ4_1 432 TGAGGAGACGGTGACCAGGGTTCCTTG GCCCCAG >IGHJSEQ4_3 433 TGAGGAGACGGTGACCAGGGTCCCTTG GCCCCAG >IGHJSEQ4_2 434 TGAGGAGACGGTGACCAGGGTTCCCTG GCCCCAG >IGHJSEQ3_12 435 CTGAAGAGACGGTGACCATTGTCCCTT GGCCCCAG >IGHJSEQ6_1 436 CTGAGGAGACGGTGACCGTGGTCCCTT GCCCCCAG >IGHJSEQ6_2 437 TGAGGAGACGGTGACCGTGGTCCCTTG GCCCCAG >IGHJSEQ6_34 438 CTGAGGAGACGGTGACCGTGGTCCCTT TGCCCCAG >IGHJSEQ2_1 439 CTGAGGAGACAGTGACCAGGGTGCCAC GGCCCCAG >IGHJSEQ5_1 440 CTGAGGAGACGGTGACCAGGGTTCCTT GGCCCCAG >IGHJSEQ5_2 441 CTGAGGAGACGGTGACCAGGGTTCCCT GGCCCCAG >IGHJSEQ1_1 442 CTGAGGAGACGGTGACCAGGGTGCCCT GGCCCCAG

Processing Sequence Data

For rapid analysis of sequencing results, an algorithm can be developed by one of ordinary skill. A preferred method is as follows.

The use of a PCR step to amplify the TCRβ CDR3 regions prior to sequencing could potentially introduce a systematic bias in the inferred relative abundance of the sequences, due to differences in the efficiency of PCR amplification of CDR3 regions utilizing different Vβ and Jβ gene segments. Each cycle of PCR amplification potentially introduces a bias of average magnitude 1.5^(1/15)=1.027. Thus, the 25 cycles of PCR introduces a total bias of average magnitude 1.027²⁵=1.95 in the inferred relative abundance of distinct CDR3 region sequences.

Sequenced reads were filtered for those including CDR3 sequences. Sequencer data processing involves a series of steps to remove errors in the primary sequence of each read, and to compress the data. A complexity filter removes approximately 20% of the sequences that are misreads from the sequencer. Then, sequences were required to have a minimum of a six base match to both one of the thirteen TCRB J-regions and one of 54 V-regions. Applying the filter to the control lane containing phage sequence, on average only one sequence in 7-8 million passed these steps. Finally, a nearest neighbor algorithm was used to collapse the data into unique sequences by merging closely related sequences, in order to remove both PCR error and sequencing error.

Analyzing the data, the ratio of sequences in the PCR product must be derived working backward from the sequence data before estimating the true distribution of clonotypes in the blood. For each sequence observed a given number of times in the data herein, the probability that that sequence was sampled from a particular size PCR pool is estimated. Because the CDR3 regions sequenced are sampled randomly from a massive pool of PCR products, the number of observations for each sequence are drawn from Poisson distributions. The Poisson parameters are quantized according to the number of T cell genomes that provided the template for PCR. A simple Poisson mixture model both estimates these parameters and places a pairwise probability for each sequence being drawn from each distribution. This is an expectation maximization method which reconstructs the abundances of each sequence that was drawn from the blood.

To estimate diversity, the “unseen species” formula is employed. To apply this formula, unique adaptive immune receptors (e.g. TCRB) clonotypes takes the place of species. The mathematical solution provides that for a total number of TCRβ “species” or clonotypes, S, a sequencing experiment observes x_(s) copies of sequence s. For all of the unobserved clonotypes, x_(s) equals 0, and each TCR clonotype is “captured” in a blood draw according to a Poisson process with parameter λ_(s). The number of T cell genomes sequenced in the first measurement 1, and in the second measurement. Since there are a large number of unique sequences, an integral will represent the sum. If G(λ) is the empirical distribution function of the parameters λ₁, . . . , λ_(s), and n_(x) is the number of clonotypes sequenced exactly x times, then the total number of clonotypes, i.e., the measurement of diversity E, is given by the following formula:

${E\left( n_{x} \right)} = {S{\int_{0}^{\infty}{\left( \frac{^{- \lambda}\lambda^{x}}{x!} \right){{{G(\lambda)}}.}}}}$

For a given experiment, where T cells are sampled from some arbitrary source (e.g. a blood draw), the formula is used to estimate the total diversity of species in the entire source. The idea is that the sampled number of clonotypes at each size contains sufficient information to estimate the underlying distribution of clonotypes in the whole source. To derive the formula, the number of new species expected if the exact measurement was repeated was estimated. The limit of the formula as if repeating the measurements an infinite number of times. The result is the expect number of species in the total underlying source population. The value for Δ(t), the number of new clonotypes observed in a second measurement, should be determined, preferably using the following equation:

${\Delta \; (t)} = {{{\sum\limits_{x}{E\left( n_{x} \right)}_{{{msmt}\; 1} + {{msmt}\; 2}}} - {\sum\limits_{x}{E\left( n_{x} \right)}_{{msmt}\; 1}}} = {S{\int_{0}^{\infty}{{^{- \lambda}\left( {1 - e^{{- \lambda}\; t}} \right)}{{G(\lambda)}}}}}}$

in which msmt1 and msmt2 are the number of clonotypes from measurement 1 and 2, respectively. Taylor expansion of 1−e^(−λt) gives Δ(t)=E(x₁)t−E(x₂)t²+E(x₃)t³− . . . , which can be approximated by replacing the expectations E(n_(x)) with the observed numbers in the first measurement. Using in the numbers observed in the first measurement, this formula predicts that 1.6*10⁵ new unique sequences should be observed in the second measurement. The actual value of the second measurement was 1.8*10⁵ new TCRβ sequences, which implies that the prediction provided a valid lower bound on total diversity. An Euler's transformation was used to regularize Δ(t) to produce a lower bound for Δ(∞).

Using a Measurement of Diversity to Diagnose Disease

The measurement of diversity can be used to diagnose disease or the effects of a treatment, as follows. T cell and/or B cell receptor repertoires can be measured at various time points, e.g., after hematopoietic stem cell transplant (HSCT) treatment for leukemia. Both the change in diversity and the overall diversity of TCRB repertoire can be utilized to measure immunocompetence. A standard for the expected rate of immune reconstitution after transplant can be utilized. The rate of change in diversity between any two time points may be used to actively modify treatment. The overall diversity at a fixed time point is also an important measure, as this standard can be used to compare between different patients. In particular, the overall diversity is the measure that should correlate with the clinical definition of immune reconstitution. This information may be used to modify prophylactic drug regiments of antibiotics, antivirals, and antifungals, e.g., after HSCT.

The assessment of immune reconstitution after allogeneic hematopoietic cell transplantation can be determined by measuring changes in diversity. These techniques will also enhance the analysis of how lymphocyte diversity declines with age, as measured by analysis of T cell responses to vaccination. Further, the methods of the invention provide a means to evaluate investigational therapeutic agents (e.g., Interleukin-7 (IL-7)) that have a direct effect on the generation, growth, and development of αβ T cells. Moreover, application of these techniques to the study of thymic T cell populations will provide insight into the processes of both T cell receptor gene rearrangement as well as positive and negative selection of thymocytes.

A newborn that does not yet have a fully functioning immune system but may have maternally transmitted antibody is immunodeficient. A newborn is susceptible to a number of diseases until its immune system autonomously develops, and our measurement of the adaptive immune system may will likely prove useful with newborn patients.

Lymphocyte diversity can be assessed in other states of congenital or acquired immunodeficiency. An AIDS patient with a failed or failing immune system can be monitored to determine the stage of disease, and to measure a patient's response to therapies aimed to reconstitute immunocompetence.

Another application of the methods of the invention is to provide diagnostic measures for solid organ transplant recipients taking medication so their body will not reject the donated organ. Generally, these patients are under immunosuppressive therapies. Monitoring the immunocompetence of the host will assist before and after transplantation.

Individuals exposed to radiation or chemotherapeutic drugs are subject to bone marrow transplantations or otherwise require replenishment of T cell populations, along with associated immunocompetence. The methods of the invention provide a means for qualitatively and quantitatively assessing the bone marrow graft, or reconstitution of lymphocytes in the course of these treatments.

One manner of determining diversity is by comparing at least two samples of genomic DNA, preferably in which one sample of genomic DNA is from a patient and the other sample is from a normal subject, or alternatively, in which one sample of genomic DNA is from a patient before a therapeutic treatment and the other sample is from the patient after treatment, or in which the two samples of genomic DNA are from the same patient at different times during treatment. Another manner of diagnosis may be based on the comparison of diversity among the samples of genomic DNA, e.g., in which the immunocompetence of a human patient is assessed by the comparison.

Biomarkers

Shared TCR sequences between individuals represent a new class of potential biomarkers for a variety of diseases, including cancers, autoimmune diseases, and infectious diseases. These are the public T cells that have been reported for multiple human diseases. TCRs are useful as biomarkers because T cells are a result of clonal expansion, by which the immune system amplifies these biomarkers through rapid cell division. Following amplification, the TCRs are readily detected even if the target is small (e.g. an early stage tumor). TCRs are also useful as biomarkers because in many cases the T cells might additionally contribute to the disease causally and, therefore could constitute a drug target. T cells self interactions are thought to play a major role in several diseases associated with autoimmunity, e.g., multiple sclerosis, Type I diabetes, and rheumatoid arthritis.

EXAMPLES Example 1 Sample Acquisition, PBMC Isolation, FACS Sorting and Genomic DNA Extraction

Peripheral blood samples from two healthy male donors aged 35 and 37 were obtained with written informed consent using forms approved by the Institutional Review Board of the Fred Hutchinson Cancer Research Center (FHCRC). Peripheral blood mononuclear cells (PBMC) were isolated by Ficoll-Hypaque® density gradient separation. The T-lymphocytes were flow sorted into four compartments for each subject: CD8⁺CD45RO^(+/−) and CD4⁺CD45RO^(+/−). For the characterization of lymphocytes the following conjugated anti-human antibodies were used: CD4 FITC (clone M-T466, Miltenyi Biotec), CD8 PE (clone RPA-T8, BD Biosciences), CD45RO ECD (clone UCHL-1, Beckman Coulter), and CD45RO APC (clone UCHL-1, BD Biosciences). Staining of total PBMCs was done with the appropriate combination of antibodies for 20 minutes at 4° C., and stained cells were washed once before analysis. Lymphocyte subsets were isolated by FACS sorting in the BD FACSAria™ cell-sorting system (BD Biosciences). Data were analyzed with FlowJo software (Treestar Inc.).

Total genomic DNA was extracted from sorted cells using the QIAamp® DNA blood Mini Kit (QIAGEN®). The approximate mass of a single haploid genome is 3 pg. In order to sample millions of rearranged TCRB in each T cell compartment, 6 to 27 micrograms of template DNA were obtained from each compartment (see Table 10).

TABLE 10 CD8+/ CD8+/ CD4+/ CD4+/ CD45RO− CD45RO+ CD45RO− CD45RO+ Donor cells (×10⁶) 9.9 6.3 6.3 10 2 DNA (μg) 27 13 19 25 PCR cycles 25 25 30 30 clusters (K/tile) 29.3 27 102.3* 118.3* VJ sequences 3.0 2.0 4.4 4.2 (×10⁶) Cells 4.9 4.8 3.3 9 1 DNA 12 13 6.6 19 PCR cycles 30 30 30 30 Clusters 116.3 121 119.5 124.6 VJ sequences 3.2 3.7 4.0 3.8 Cells NA NA NA 0.03 PCR DNA NA NA NA 0.015 Bias PCR cycles NA NA NA 25 + 15 assess- clusters NA NA NA 1.4/23.8 ment VJ sequences NA NA NA 1.6

Example 2 Virtual T Cell Receptor β Chain Spectratyping

Virtual TCR β chain spectratyping was performed as follows. Complementary DNA was synthesized from RNA extracted from sorted T cell populations and used as template for multiplex PCR amplification of the rearranged TCR β chain CDR3 region. Each multiplex reaction contained a 6-FAM-labeled antisense primer specific for the TCR β chain constant region, and two to five TCR β chain variable (TRBV) gene-specific sense primers. All 23 functional Vβ families were studied. PCR reactions were carried out on a Hybaid PCR Express thermal cycler (Hybaid, Ashford, UK) under the following cycling conditions: 1 cycle at 95° C. for 6 minutes, 40 cycles at 94° C. for 30 seconds, 58° C. for 30 seconds, and 72° C. for 40 seconds, followed by 1 cycle at 72° C. for 10 minutes. Each reaction contained cDNA template, 500 μM dNTPs, 2 mM MgCl₂ and 1 unit of AmpliTaq Gold DNA polymerase (Perkin Elmer) in AmpliTaq Gold buffer, in a final volume of 20 μl. After completion, an aliquot of the PCR product was diluted 1:50 and analyzed using a DNA analyzer. The output of the DNA analyzer was converted to a distribution of fluorescence intensity vs. length by comparison with the fluorescence intensity trace of a reference sample containing known size standards.

Example 3 Multiplex PCR Amplification of TCRβ CDR3 Regions

The CDR3 junction region was defined operationally, as follows. The junction begins with the second conserved cysteine of the V-region and ends with the conserved phenylalanine of the J-region. Taking the reverse complements of the observed sequences and translating the flanking regions, the amino acids defining the junction boundaries were identified. The number of nucleotides between these boundaries determines the length and therefore the frame of the CDR3 region. In order to generate the template library for sequencing, a multiplex PCR system was selected to amplify rearranged TCRβ loci from genomic DNA. The multiplex PCR system uses 45 forward primers (Table 3), each specific to a functional TCR Vβ segment, and thirteen reverse primers (Table 4), each specific to a TCR Jβ segment. The primers were selected to provide that adequate information is present within the amplified sequence to identify both the V and J genes uniquely (>40 base pairs of sequence upstream of the V gene recombination signal sequence (RSS), and >30 base pairs downstream of the J gene RSS).

The forward primers are modified at the 5′ end with the universal forward primer sequence compatible with the Illumina GA2 cluster station solid-phase PCR. Similarly, all of the reverse primers are modified with the GA2 universal reverse primer sequence. The 3′ end of each forward primer is anchored at position −43 in the Vβ segment, relative to the recombination signal sequence (RSS), thereby providing a unique Vβ tag sequence within the amplified region. The thirteen reverse primers specific to each Jβ segment are anchored in the 3′ intron, with the 3′ end of each primer crossing the intron/exon junction. Thirteen sequencing primers complementary to the Jβ segments were designed that are complementary to the amplified portion of the Jβ segment, such that the first few bases of sequence generated will capture the unique Jβ tag sequence.

On average J deletions were 4 bp+/−2.5 bp, which implies that J deletions greater than 10 nucleotides occur in less than 1% of sequences. The thirteen different TCR JP gene segments each had a unique four base tag at positions +11 through +14 downstream of the RSS site. Thus, sequencing oligonucleotides were designed to anneal to a consensus nucleotide motif observed just downstream of this “tag”, so that the first four bases of a sequence read will uniquely identify the J segment (Table 5).

The information used to assign the J and V segment of a sequence read is entirely contained within the amplified sequence, and does not rely upon the identity of the PCR primers. These sequencing oligonucleotides were selected such that promiscuous priming of a sequencing reaction for one J segment by an oligonucleotide specific to another J segment would generate sequence data starting at exactly the same nucleotide as sequence data from the correct sequencing oligonucleotide. In this way, promiscuous annealing of the sequencing oligonucleotides did not impact the quality of the sequence data generated.

The average length of the CDR3 region, defined following convention as the nucleotides between the second conserved cysteine of the V segment and the conserved phenylalanine of the J segment, is 35+/−3, so sequences starting from the Jβ segment tag will nearly always capture the complete VNDNJ junction in a 50 bp read.

TCR βJ gene segments are roughly 50 bp in length. PCR primers that anneal and extend to mismatched sequences are referred to as promiscuous primers. Because of the risk of promiscuous priming in the context of multiplex PCR, especially in the context of a gene family, the TCR Jβ Reverse PCR primers were designed to minimize overlap with the sequencing oligonucleotides. Thus, the 13 TCR Jβ reverse primers are anchored at the 3′ end on the consensus splice site motif, with minimal overlap of the sequencing primers. The TCR Jβ primers were designed for a consistent annealing temperature (58 degrees in 50 mM salt) using the OligoCalc program under default parameters (http://www.basic.northwestern.edu/biotools/oligocalc.html).

The 45 TCR Vβ forward primers were designed to anneal to the Vβ segments in a region of relatively strong sequence conservation between Vβ segments, for two express purposes. First, maximizing the conservation of sequence among these primers minimizes the potential for differential annealing properties of each primer. Second, the primers were chosen such that the amplified region between V and J primers will contain sufficient TCR Vβ sequence information to identify the specific Vβ gene segment used. This obviates the risk of erroneous TCR Vβ gene segment assignment, in the event of promiscuous priming by the TCR Vβ primers. TCR Vβ forward primers were designed for all known non-pseudogenes in the TCRβ locus.

The total PCR product for a successfully rearranged TCRβ CDR3 region using this system is expected to be approximately 200 bp long. Genomic templates were PCR amplified using an equimolar pool of the 45 TCR Vβ F primers (the “VF pool”) and an equimolar pool of the thirteen TCR Jβ R primers (the “JR pool”). 50 μl PCR reactions were set up at 1.0 μM VF pool (22 nM for each unique TCR Vβ F primer), 1.0 μM JR pool (77 nM for each unique TCRBJR primer), 1×QIAGEN Multiple PCR master mix (QIAGEN part number 206145), 10% Q-solution (QIAGEN), and 16 ng/ul gDNA. The following thermal cycling conditions were used in a PCR Express thermal cycler (Hybaid, Ashford, UK) under the following cycling conditions: 1 cycle at 95° C. for 15 minutes, 25 to 40 cycles at 94° C. for 30 seconds, 59° C. for 30 seconds and 72° C. for 1 minute, followed by one cycle at 72° C. for 10 minutes. 12-20 wells of PCR were performed for each library, in order to sample hundreds of thousands to millions of rearranged TCRβ CDR3 loci.

Example 4 Pre-Processing of Sequence Data

Sequencer data processing involves a series of steps to remove errors in the primary sequence of each read, and to compress the data. First, a complexity filter removes approximately 20% of the sequences which are misreads from the sequencer. Then, sequences were required to have a minimum of a six base match to both one of the thirteen J-regions and one of 54 V-regions. Applying the filter to the control lane containing phage sequence, on average only one sequence in 7-8 million passed these steps without false positives. Finally, a nearest neighbor algorithm was used to collapse the data into unique sequences by merging closely related sequences, in order to remove both PCR error and sequencing error (see Table 10).

Example 5 Estimating Relative CDR3 Sequence Abundance in PCR Pools and Blood samples

After collapsing the data, the underlying distribution of T-cell sequences in the blood reconstructing were derived from the sequence data. The procedure used three steps; 1) flow sorting T-cells drawn from peripheral blood, 2) PCR amplification, and 3) sequencing. Analyzing the data, the ratio of sequences in the PCR product must be derived working backward from the sequence data before estimating the true distribution of clonotypes in the blood.

For each sequence observed a given number of times in the data herein, the probability that that sequence was sampled from a particular size PCR pool is estimated. Because the CDR3 regions sequenced are sampled randomly from a massive pool of PCR products, the number of observations for each sequence are drawn from Poisson distributions. The Poisson parameters are quantized according to the number of T cell genomes that provided the template for PCR. A simple Poisson mixture model both estimates these parameters and places a pairwise probability for each sequence being drawn from each distribution. This is an expectation maximization method which reconstructs the abundances of each sequence that was drawn from the blood.

Example 6 Unseen Species Model for Estimation of True Diversity

A mixture model can reconstruct the frequency of each TCRβ CDR3 species drawn from the blood, but the larger question is how many unique CDR3 species were present in the donor? This is a fundamental question that needs to be answered as the available sample is limited in each donor, and will be more important in the future as these techniques are extrapolated to the smaller volumes of blood that can reasonably be drawn from patients undergoing treatment.

The mathematical solution provides that for a total number of TCRβ “species” or clonotypes, S, a sequencing experiment observes x_(s) copies of sequence s. For all of the unobserved clonotypes, x_(s) equals 0, and each TCR clonotype is “captured” in a blood draw according to a Poisson process with parameter λ_(s). The number of T cell genomes sequenced in the first measurement 1, and in the second measurement. Since there are a large number of unique sequences, an integral will represent the sum. If G(λ) is the empirical distribution function of the parameters λ₁, . . . , λ_(s), and n_(x) is the number of clonotypes sequenced exactly x times, then

${E\left( n_{x} \right)} = {S{\int_{0}^{\infty}{\left( \frac{^{- \lambda}\lambda^{x}}{x!} \right){{{G(\lambda)}}.}}}}$

The value Δ(t) is the number of new clonotypes observed in the second sequencing experiment.

${\Delta (t)} = {{{\sum\limits_{x}{E\left( n_{x} \right)}_{{{ex}\; p\; 1} + {{ex}\; p\; 2}}} - {\sum\limits_{x}{E\left( n_{x} \right)}_{{ex}\; p\; 1}}} = {S{\int_{0}^{\infty}{{^{- \lambda}\left( {1 - ^{{- \lambda}\; t}} \right)}{{G(\lambda)}}}}}}$

Taylor expansion of 1−e^(−λt) gives Δ(t)=E(x₁)t−E(x₂)t²+E(x₃)t³− . . . , which can be approximated by replacing the expectations (E(n_(x))) with the observed numbers in the first measurement. Using in the numbers observed in the first measurement, this formula predicts that 1.6*10⁵ new unique sequences should be observed in the second measurement. The actual value of the second measurement was 1.8*10⁵ new TCRβ sequences, which implies that the prediction provided a valid lower bound on total diversity. An Euler's transformation was used to regularize Δ(t) to produce a lower bound for Δ(∞).

Example 7 Error Correction and Bias Assessment

Sequence error in the primary sequence data derives primarily from two sources: (1) nucleotide misincorporation that occurs during the amplification by PCR of TCRβ CDR3 template sequences, and (2) errors in base calls introduced during sequencing of the PCR-amplified library of CDR3 sequences. The large quantity of data allows us to implement a straightforward error correcting code to correct most of the errors in the primary sequence data that are attributable to these two sources. After error correction, the number of unique, in-frame CDR3 sequences and the number of observations of each unique sequence were tabulated for each of the four flow-sorted T cell populations from the two donors. The relative frequency distribution of CDR3 sequences in the four flow cytometrically-defined populations demonstrated that antigen-experienced CD45RO⁺ populations contained significantly more unique CDR3 sequences with high relative frequency than the CD45RO⁻ populations. Frequency histograms of TCRβ CDR3 sequences observed in four different T cell subsets distinguished by expression of CD4, CD8, and CD45RO and present in blood showed that ten unique sequences were each observed 200 times in the CD4⁺CD45RO⁺ (antigen-experienced) T cell sample, which was more than twice as frequent as that observed in the CD4⁺CD45RO⁻ populations.

The use of a PCR step to amplify the TCRβ CDR3 regions prior to sequencing could potentially introduce a systematic bias in the inferred relative abundance of the sequences, due to differences in the efficiency of PCR amplification of CDR3 regions utilizing different Vβ and Jβ gene segments. To estimate the magnitude of any such bias, the TCRβ CDR3 regions from a sample of approximately 30,000 unique CD4⁺CD45RO⁺ T lymphocyte genomes were amplified through 25 cycles of PCR, at which point the PCR product was split in half. Half was set aside, and the other half of the PCR product was amplified for an additional 15 cycles of PCR, for a total of 40 cycles of amplification. The PCR products amplified through 25 and 40 cycles were then sequenced and compared. Over 95% of the 25 cycle sequences were also found in the 40-cycle sample: a linear correlation is observed when comparing the frequency of sequences between these samples. For sequences observed a given number of times in the 25 cycle lane, a combination of PCR bias and sampling variance accounts for the variance around the mean of the number of observations at 40 cycles. Conservatively attributing the mean variation about the line (1.5-fold) entirely to PCR bias, each cycle of PCR amplification potentially introduces a bias of average magnitude 1.5^(1/15)=1.027. Thus, the 25 cycles of PCR introduces a total bias of average magnitude 1.027²⁵=1.95 in the inferred relative abundance of distinct CDR3 region sequences.

Example 8 JP Gene Segment Usage

The CDR3 region in each TCR 13 chain includes sequence derived from one of the thirteen J_(β) gene segments. Analysis of the CDR3 sequences in the four different T cell populations from the two donors demonstrated that the fraction of total sequences which incorporated sequences derived from the thirteen different J_(β) gene segments varied more than 20-fold. Jβ utilization among four different T flow cytometrically-defined T cells from a single donor is was relatively constant within a given donor. Moreover, the J_(β) usage patterns observed in two donors, which were inferred from analysis of genomic DNA from T cells sequenced using the GA, are qualitatively similar to those observed in T cells from umbilical cord blood and from healthy adult donors, both of which were inferred from analysis of cDNA from T cells sequenced using exhaustive capillary-based techniques.

Example 9 Nucleotide Insertion Bias

Much of the diversity at the CDR3 junctions in TCR α and β chains is created by non-templated nucleotide insertions by the enzyme Terminal Deoxynucloetidyl Transferase (TdT). However, in vivo, selection plays a significant role in shaping the TCR repertoire giving rise to unpredictability. The TdT nucleotide insertion frequencies, independent of selection, were calculated using out of frame TCR sequences. These sequences are non-functional rearrangements that are carried on one allele in T cells where the second allele has a functional rearrangement. The mono-nucleotide insertion bias of TdT favors C and G (Table 11).

TABLE 11 Mono-nucleotide bias in out of frame data A C G T Lane 1 0.24 0.294 0.247 0.216 Lane 2 0.247 0.284 0.256 0.211 Lane 3 0.25 0.27 0.268 0.209 Lane 4 0.255 0.293 0.24 0.21

Similar nucleotide frequencies are observed in the in frame sequences (Table 12).

TABLE 12 Mono-nucleotide bias in in-frame data A C G T Lane 1 0.21 0.285 0.275 0.228 Lane 2 0.216 0.281 0.266 0.235 Lane 3 0.222 0.266 0.288 0.221 Lane 4 0.206 0.294 0.228 0.27

The N regions from the out of frame TCR sequences were used to measure the di-nucleotide bias. To isolate the marginal contribution of a di-nucleotide bias, the di-nucleotide frequencies was divided by the mononucleotide frequencies of each of the two bases. The measure is

$m = {\frac{f\left( {n_{1}n_{2}} \right)}{{f\left( n_{1} \right)}{f\left( n_{2} \right)}}.}$

The matrix for m is found in Table 13.

TABLE 13 Di-nucleotide odd ratios for out of frame data A C G T A 1.198 0.938 0.945 0.919 C 0.988 1.172 0.88 0.931 G 0.993 0.701 1.352 0.964 T 0.784 1.232 0.767 1.23

Many of the dinucleotides are under or over represented. As an example, the odds of finding a GG pair are very high. Since the codons GGN translate to glycine, many glycines are expected in the CDR3 regions.

Example 10 Amino Acid Distributions in the CDR3 Regions

The distribution of amino acids in the CDR3 regions of TCRβ chains are shaped by the germline sequences for V, D, and J regions, the insertion bias of TdT, and selection. The distribution of amino acids in this region for the four different T cell sub-compartments is very similar between different cell subtypes. Separating the sequences into β chains of fixed length, a position dependent distribution among amino acids, which are grouped by the six chemical properties: small, special, and large hydrophobic, neutral polar, acidic and basic. The distributions are virtually identical except for the CD8+ antigen experienced T cells, which have a higher proportion of acidic bases, particularly at position 5.

Of particular interest is the comparison between CD8⁺ and CD4⁺ TCR sequences as they bind to peptides presented by class I and class II HLA molecules, respectively. The CD8⁺ antigen experienced T cells have a few positions with a higher proportion of acidic amino acids. This could be do binding with a basic residue found on HLA Class I molecules, but not on Class II.

Example 11 TCR β Chains with Identical Amino Acid Sequences Found in Different People

The TCR β chain sequences were translated to amino acids and then compared pairwise between the two donors. Many thousands of exact sequence matches were observed. For example, comparing the CD4⁺CD45RO⁻ sub-compartments, approximately 8,000 of the 250,000 unique amino acid sequences from donor 1 were exact matches to donor 2. Many of these matching sequences at the amino acid level have multiple nucleotide differences at third codon positions. Following the example mentioned above, 1,500/8,000 identical amino acid matches had >5 nucleotide mismatches. Between any two T cell sub-types, 4-5% of the unique TCRβ sequences were found to have identical amino acid matches.

Two possibilities were examined: that 1) selection during TCR development is producing these common sequences and 2) the large bias in nucleotide insertion frequency by TdT creates similar nucleotide sequences. The in-frame pairwise matches were compared to the out-of-frame pairwise matches (see Examples 1-4, above). Changing frames preserved all of the features of the genetic code and so the same number of matches should be found if the sequence bias was responsible for the entire observation. However, almost twice as many in-frame matches as out-of-frame matches were found, suggesting that selection at the protein level is playing a significant role.

To confirm this finding of thousands of identical TCR β chain amino acid sequences, two donors were compared with respect to the CD8⁺CD62L⁺CD45RA⁺ (naïve-like) TCRs from a third donor, a 44 year old CMV⁺ Caucasian female. Identical pairwise matches of many thousands of sequences at the amino acid level between the third donor and each of the original two donors were found. In contrast, 460 sequences were shared between all three donors. The large variation in total number of unique sequences between the donors is a product of the starting material and variations in loading onto the sequencer, and is not representative of a variation in true diversity in the blood of the donors.

Example 12 Higher Frequency Clonotypes are Closer to Germline

The variation in copy number between different sequences within every T cell sub-compartment ranged by a factor of over 10,000-fold. The only property that correlated with copy number was (the number of insertions plus the number of deletions), which inversely correlated. Results of the analysis showed that deletions play a smaller role than insertions in the inverse correlation with copy number.

Sequences with less insertions and deletions have receptor sequences closer to germ line. One possibility for the increased number of sequences closer to germ line is that they are the created multiple times during T cell development. Since germ line sequences are shared between people, shared TCRβ chains are likely created by TCRs with a small number of insertions and deletions.

Example 13 “Spectratype” Analysis of TCRβ CDR3 Sequences by V Gene Segment Utilization and CDR3 Length

TCR diversity has commonly been assessed using the technique of TCR spectratyping, an RT-PCR-based technique that does not assess TCR CDR3 diversity at the sequence level, but rather evaluates the diversity of TCRα or TCRβ CDR3 lengths expressed as mRNA in subsets of αβ T cells that use the same V_(α) or V_(β) gene segment. The spectratypes of polyclonal T cell populations with diverse repertoires of TCR CDR3 sequences, such as are seen in umbilical cord blood or in peripheral blood of healthy young adults typically contain CDR3 sequences of 8-10 different lengths that are multiples of three nucleotides, reflecting the selection for in-frame transcripts. Spectratyping also provides roughly quantitative information about the relative frequency of CDR3 sequences with each specific length. To assess whether direct sequencing of TCRβ CDR3 regions from T cell genomic DNA using the sequencer could faithfully capture all of the CDR3 length diversity that is identified by spectratyping, “virtual” TCRβ spectratypes (see Examples above) were generated from the sequence data and compared with TCRβ spectratypes generated using conventional PCR techniques. The virtual spectratypes contained all of the CDR3 length and relative frequency information present in the conventional spectratypes. Direct TCRβ CDR3 sequencing captures all of the TCR diversity information present in a conventional spectratype. A comparison of standard TCRβ spectratype data and calculated TCRβ CDR3 length distributions for sequences utilizing representative TCR Vβ gene segments and present in CD4⁺CD45RO⁺ cells from donor 1. Reducing the information contained in the sequence data to a frequency histogram of the unique CDR3 sequences with different lengths within each Vβ family readily reproduces all of the information contained in the spectratype data. In addition, the virtual spectratypes revealed the presence within each V_(β) family of rare CDR3 sequences with both very short and very long CDR3 lengths that were not detected by conventional PCR-based spectratyping.

Example 14 Estimation of Total CDR3 Sequence Diversity

After error correction, the number of unique CDR3 sequences observed in each lane of the sequencer flow cell routinely exceeded 1×10⁵. Given that the PCR products sequenced in each lane were necessarily derived from a small fraction of the T cell genomes present in each of the two donors, the total number of unique TCRβ CDR3 sequences in the entire T cell repertoire of each individual is likely to be far higher. Estimating the number of unique sequences in the entire repertoire, therefore, requires an estimate of the number of additional unique CDR3 sequences that exist in the blood but were not observed in the sample. The estimation of total species diversity in a large, complex population using measurements of the species diversity present in a finite sample has historically been called the “unseen species problem” (see Examples above). The solution starts with determining the number of new species, or TCRβ CDR3 sequences, that are observed if the experiment is repeated, i.e., if the sequencing is repeated on an identical sample of peripheral blood T cells, e.g., an identically prepared library of TCRβ CDR3 PCR products in a different lane of the sequencer flow cell and counting the number of new CDR3 sequences. For CD8⁺CD45RO⁻ cells from donor 2, the predicted and observed number of new CDR3 sequences in a second lane are within 5% (see Examples above), suggesting that this analytic solution can, in fact, be used to estimate the total number of unique TCRβ CDR3 sequences in the entire repertoire.

The resulting estimates of the total number of unique TCRβ CDR3 sequences in the four flow cytometrically-defined T cell compartments are shown in Table 14.

TABLE 14 TCR repertoire diversity Donor CD8 CD4 CD45RO Diversity 1 + − +  6.3 * 10⁵ + − − 1.24 * 10⁶ − + +  8.2 * 10⁵ − + − 1.28 * 10⁶ Total T cell diversity 3.97 * 10⁶ 2 + − +  4.4 * 10⁵ + − −  9.7 * 10⁵ − + +  8.7 * 10⁵ − + − 1.03 * 10⁶ Total T cell diversity 3.31 * 10⁶

Of note, the total TCRβ diversity in these populations is between 3-4 million unique sequences in the peripheral blood. Surprisingly, the CD45RO⁺, or antigen-experienced, compartment constitutes approximately 1.5 million of these sequences. This is at least an order of magnitude larger than expected. This discrepancy is likely attributable to the large number of these sequences observed at low relative frequency, which could only be detected through deep sequencing. The estimated TCRβ CDR3 repertoire sizes of each compartment in the two donors are within 20% of each other.

The results herein demonstrate that the realized TCRβ receptor diversity is at least five-fold higher than previous estimates (˜4*10⁶ distinct CDR3 sequences), and, in particular, suggest far greater TCRβ diversity among CD45RO⁺ antigen-experienced αβ T cells than has previously been reported (˜1.5*10⁶ distinct CDR3 sequences). However, bioinformatic analysis of the TCR sequence data shows strong biases in the mono- and di-nucleotide content, implying that the utilized TCR sequences are sampled from a distribution much smaller than the theoretical size. With the large diversity of TCRβ chains in each person sampled from a severely constrict space of sequences, overlap of the TCR sequence pools can be expected between each person. In fact, the results showed about 5% of CD8⁺ naïve TCRβ chains with exact amino acid matches are shared between each pair of three different individuals. As the TCRα pool has been previously measured to be substantially smaller than the theoretical TCRβ diversity, these results show that hundreds to thousands of truly public αβ TCRs can be found. 

What is claimed:
 1. A method for monitoring a disease state of an individual by determining one or more correlating clonotypes of the disease wherein the disease comprises an autoimmune disease, an infectious disease or cancer, the method comprising: (a) generating one or more clonotype profiles by sequencing individual, spatially isolated nucleic acid molecules, i. wherein each spatially isolated nucleic acid molecule comprises a recombined sequence from a T-cell and/or B-cell from at least one sample from the individual, ii. wherein each of said samples comprises T-cells and/or B-cells and wherein the at least one sample is from a tissue affected by the disease, and iii. wherein each of the one or more clonotype profiles comprises at least 1000 sequence reads of at least 30 base pairs per read; (b) determining one or more correlating clonotypes of the disease in the individual using the one or more clonotype profiles from the tissue affected by the disease; (c) generating one or more clonotype profiles of one or more blood samples of the individual, wherein each of the one or more clonotype profiles of the one or more blood samples comprises at least 1000 sequence reads of at least 30 base pairs per read; and (d) monitoring a level of the one or more correlating clonotypes in the one or more blood samples of the individual by comparing the one or more correlating clonotypes identified in step (b) with the one or more clonotype profiles generated in step (c).
 2. The method of claim 1, wherein the determining one or more correlating clonotypes of the disease in the individual is achieved by comparing the clonotype profile from the tissue affected by the disease with one or more other clonotype profiles.
 3. The method of claim 1, wherein the determining one or more correlating clonotypes of the disease in the individual is achieved by identifying high level clonotypes in the clonotype profile from the tissue affected by the disease.
 4. The method of claim 1, wherein said disease is an autoimmune disease and said one or more correlating clonotypes are present in a peak state of the disease, wherein the peak state of the disease is a flare state of the autoimmune disease.
 5. The method of claim 1, wherein said T-cells and/or B-cells comprise a subset of T-cells and/or B cells.
 6. The method of claim 5, wherein said subset of T-cells and/or B-cells are enriched by interaction with a marker.
 7. The method of claim 6, wherein said marker is a cell surface marker on the subset of T-cells and/or B-cells.
 8. The method of claim 5, wherein said subset of T-cells and/or B-cells interact with an antigen specifically present in the disease.
 9. The method of claim 1, wherein the disease is systemic lupus erythematosus or multiple sclerosis.
 10. The method of claim 1, wherein said nucleic acid molecules are amplified.
 11. The method of claim 10, wherein each of said samples comprises at least 10,000 B-cells.
 12. The method of claim 10, wherein each of said samples comprises at least 10,000 T-cells.
 13. The method of claim 1, wherein each of said samples comprises at least 10,000 B-cells and wherein each of said clonotype profiles comprises at least 100,000 sequence reads.
 14. The method of claim 13, wherein each of said sequence reads comprises a paired-end read having an error rate of one percent or more.
 15. The method of claim 1, wherein each of said samples comprises at least 10,000 T-cells and wherein each of said clonotype profiles comprises at least 100,000 sequence reads.
 16. The method of claim 15, wherein each of said sequence reads comprises a paired-end read having an error rate of one percent or more.
 17. A method for monitoring a disease of an individual by quantifying a diversity of an immune receptor repertoire of said individual, wherein said disease comprises an autoimmune disease, an infectious disease, or cancer, said method comprising: (a) quantifying diversity of one or more immune receptor repertoires by sequencing by synthesis using an array of spatially isolated, individual DNA molecules, (i) wherein each spatially isolated, individual DNA molecule comprises a rearranged sequence from a T-cell and/or a B-cell from at least one sample obtained from said individual, (ii) wherein each of said samples comprises T cells and/or B cells and wherein said at least one sample is obtained from a tissue affected by said disease, and (iii) wherein said diversity of said one or more immune receptor repertoires comprises at least 10⁴ unique rearranged sequences of at least 30 base pairs per read; (b) determining one or more measurements of diversity of said one or more immune receptor repertoires of said disease in said individual using said one or more quantified diversities determined from said tissue affected by said disease; (c) determining one or more measurements of diversity of one or more immune receptor repertoires from blood samples obtained from said individual, wherein each of said measurements of diversity of said blood samples comprises at least 10⁴ sequence reads of at least 30 base pairs per read; and (d) monitoring a change in diversity in said one or more measurements of diversity from said blood samples of said individual by comparing said one or more measurements of diversity determined in step (b) with said one or more measurements of diversity determined in step (c).
 18. The method of claim 17, wherein said determining one or more measurements of diversity of said immune receptor repertoire of said disease in said individual comprises comparing a measurement of diversity measured from a sample comprising tissue affected by said disease with one or more other measurements of diversity.
 19. The method of claim 17, wherein said disease is an autoimmune disease and wherein said one or more measurements of diversity are measured before a patient undergoes treatment.
 20. The method of claim 17, wherein said T-cells and/or B-cells comprise a subset of T-cells and/or B-cells.
 21. The method of claim 20, wherein said subset of T-cells and/or B-cells comprise a marker.
 22. The method of claim 21, wherein said marker is a cell surface marker on the subset of T-cells and/or B-cells.
 23. The method of claim 20, wherein said subset of T-cells and/or B-cells interact with an antigen specifically present in the disease.
 24. The method of claim 17, wherein said disease is an autoimmune disease.
 25. The method of claim 17, wherein said individual DNA molecules are amplified.
 26. The method of claim 25, wherein each of said samples comprises at least 10,000 B cells.
 27. The method of claim 25, wherein each of said samples comprises at least 10,000 T cells.
 28. The method of claim 17, wherein each of said samples comprises at least 10,000 B cells, and wherein each of said measurements of diversity comprises at least 100,000 sequence reads.
 29. The method of claim 28, wherein each of said sequence reads comprise a read having an error rate of one percent or more.
 30. The method of claim 17, wherein each of said samples comprises at least 10,000 T-cells and wherein each of said measurements of diversity comprises at least 100,000 sequence reads.
 31. The method of claim 30, wherein each of said sequence reads comprise a read having an error rate of one percent or more. 